title: Temporal and spatial dispersion of human body temperature during deep hypothermia.
abstract: BACKGROUND
  Clinical temperature management remains challenging. Choosing the right sensor location to determine the core body temperature is a particular matter of academic and clinical debate. This study aimed to investigate the relationship of measured temperatures at different sites during surgery in deep hypothermic patients.
  
  
  METHODS
  In this prospective single-centre study, we studied 24 patients undergoing cardiothoracic surgery: 12 in normothermia, 3 in mild, and 9 in deep hypothermia. Temperature recordings of a non-invasive heat flux sensor at the forehead were compared with the arterial outlet temperature of a heart-lung machine, with the temperature on a conventional vesical bladder thermistor and, for patients undergoing deep hypothermia, with oesophageal temperature.
  
  
  RESULTS
  Using a linear model for sensor comparison, the arterial outlet sensor showed a difference among the other sensor positions between -0.54 and -1.12°C. The 95% confidence interval ranged between 7.06 and 8.82°C for the upper limit and -8.14 and -10.62°C for the lower limit. Because of the hysteretic shape, the curves were divided into phases and fitted into a non-linear model according to time and placement of the sensors. During cooling and warming phases, a quadratic relationship could be observed among arterial, oesophageal, vesical, and cranial temperature recordings, with coefficients of determination ranging between 0.95 and 0.98 (standard errors of the estimate 0.69-1.12°C).
  
  
  CONCLUSIONS
  We suggest that measured surrogate temperatures as indices of the cerebral temperature (e.g. vesical bladder temperature) should be interpreted with respect to the temporal and spatial dispersion during cooling and rewarming phases.
authors.affiliation: Charité Centrum für Grundlagenmedizin, Institut für Physiologie, Thielallee 71, Berlin 14195, Germany.
authors.forename: O
authors.initials: O
authors.lastname: Opatz
authors.forename: T
authors.initials: T
authors.lastname: Trippel
authors.forename: A
authors.initials: A
authors.lastname: Lochner
authors.forename: A
authors.initials: A
authors.lastname: Werner
authors.forename: A
authors.initials: A
authors.lastname: Stahn
authors.forename: M
authors.initials: M
authors.lastname: Steinach
authors.forename: J
authors.initials: J
authors.lastname: Lenk
authors.forename: H
authors.initials: H
authors.lastname: Kuppe
authors.forename: H C
authors.initials: HC
authors.lastname: Gunga
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000768:Anesthesia, General; D001790:Blood Physiological Phenomena; D001831:Body Temperature / Q000502:physiology*; D006348:Cardiac Surgical Procedures; D050792:Circulatory Arrest, Deep Hypothermia Induced; D017548:Echocardiography, Transesophageal; D004947:Esophagus / Q000502:physiology; D005260:Female; D005546:Forehead / Q000502:physiology; D006331:Heart Diseases / Q000601:surgery; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D016343:Monitoring, Intraoperative; D017711:Nonlinear Dynamics; D011446:Prospective Studies; D012881:Skin Temperature; D019616:Thoracic Surgical Procedures; D001743:Urinary Bladder / Q000502:physiology
keywords: body temperature; deep hypothermics circulatory arrest; intraoperative monitoring; mathematical model; non-linear model